本文以 Lee et al. (2015) 所提出考慮平滑性結構變化及橫斷面相依的 BCIPS 追蹤資料單根檢定,來實證探討 20 個 OECD 會員國的失業率,以判斷 OECD 國家失業率支持自然率假說或磁滯性假說,以作為政府制定政策的參考。

實證結果顯示,傳統單根檢定下 (ADF、PP、KPSS) 出現了不同的結果,可能原因為傳統單根檢定在小樣本下有檢定力不足的問題,影響實證的可信度。在第一代及第二代追蹤資料單根檢定下,IPS 及 LLC 追蹤資料單根檢定都出現了拒絕單根的虛無假設,而 CIPS 追蹤資料單根檢定結果顯示接受單根的虛無假設。但是第一代及第二代追蹤資料單根檢定都未考慮結構性變化,可能導致錯誤的結論。最後,根據考慮結構性變化及橫斷面相依的BCIPS 追蹤資料單根檢定結果得出 OECD 國家失業率是接受單根的虛無假設,因此本文認為 OECD 國家失業率傾向支持磁滯性假說。

This paper applies a newly-developed BCIPS panel unit root test that take smoothing structural breaks and cross-section dependence into account, proposed by Lee et al. (2015). We examine whether properties of the unemployment rates in twenty OECD countries can be explained by the natural rate hypothesis or hysteresis hypothesis, it will be able to provide an important reference for government policy.

In the empirical results, the insufficient power in small number of samples appeared different results in the traditional unit root tests (ADF, PP and KPSS test). In the first and second generation panel data unit root tests, the IPS and LLC tests indicate that the unemployment rates in the OECD countries can be the stationary process; the CIPS test indicates that the unemployment rates in OECD countries can be the unit root process. However, first and second generation panel data unit root tests that do not take structural break into consideration may cause wrong results. The BCIPS test indicates that the unemployment rates in OECD countries can be the unit root process. Overall, my study concludes that the unemployment rates in OECD countries can be best described as unit root process in line with the hysteresis hypothesis.

目錄
論文審定書 i
論文公開授權書 ii
誌謝 iii
摘要 iv
Abstract v
目錄 vi
圖次 viii
表次 ix
1 緒論 1
1.1 研究動機及目的 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 文獻回顧 4
2.1 單根檢定回顧 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 DF 單根檢定 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 ADF 單根檢定 . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.3 PP 單根檢定 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.4 KPSS 單根檢定 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.5 最小 LM 單根檢定 . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.6 第一代追蹤資料單根檢定 -LLC 單根檢定 . . . . . . . . . . . . . 9
2.1.7 第一代追蹤資料單根檢定 -IPS 單根檢定 . . . . . . . . . . . . . . 10
2.1.8 第二代追蹤資料單根檢定 -CIPS 單根檢定 . . . . . . . . . . . . . 11
2.2 失業率理論文獻回顧 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 失業率實證文獻回顧 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 研究方法 18
3.1 模型介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 多因子考慮跨個體間相關的追蹤資料單根檢定 . . . . . . . . . . . . . . . 19
3.3 以 BCADF 為基礎的追蹤資料單根檢定 . . . . . . . . . . . . . . . . . . 21
3.4 具序列相關的殘差項 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 選擇傅立葉頻率 κ 及落後期數 p 之方法 . . . . . . . . . . . . . . . . . 23
4 實證結果與分析 24
4.1 資料來源與說明 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2 傳統單根檢定實證結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.3 第一代追蹤資料單根檢定實證結果 . . . . . . . . . . . . . . . . . . . . . 28
4.4 第二代追蹤資料單根檢定及 BCIPS 實證結果 . . . . . . . . . . . . . . . 28
5 結論 30
參考文獻 32
附錄 38

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